Towards a Decision Support System for Big Data Projects

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Cite as text

						@Select Types{,
							 
							 
							 
							 
							 
							Journal   = "Band-1",
							 Title= "Towards a Decision Support System for Big Data Projects", 
							Author= "Matthias Volk, Daniel Staegemann, Sascha Bosse, Abdulrahman Nahhas, Klaus Turowski", 
							Doi= "https://doi.org/10.30844/wi_2020_c11-volk", 
							 Abstract= "Big data has proved to be one of the most promising trends in recent years. However, many challenges and barriers still exist, especially when it comes to the strategic planning and realization of those kinds of projects. Most of all, the selection and combination of the domain–related technologies represents a sophisticated endeavor that increases the complexity of creating a big data system. Hence, it is not surprising that the demand for experts in this area is steadily increasing. To overcome this problem and the related shortage of required knowledge, in the following paper the concept of a decision support system for the selection of appropriate big data technologies is introduced, in order to implement a given project. Through the use of the design science research methodology a preliminary artifact was developed that provides sophisticated recommendations as well as architectural models and blank systems to support the systems engineering procedure.

", 
							 Keywords= "Big Data, Technologies, Decision Support, System, Design Science", 
							}
					
Matthias Volk, Daniel Staegemann, Sascha Bosse, Abdulrahman Nahhas, Klaus Turowski: Towards a Decision Support System for Big Data Projects. Online: https://doi.org/10.30844/wi_2020_c11-volk (Abgerufen 17.07.24)

Abstract

Abstract

Big data has proved to be one of the most promising trends in recent years. However, many challenges and barriers still exist, especially when it comes to the strategic planning and realization of those kinds of projects. Most of all, the selection and combination of the domain–related technologies represents a sophisticated endeavor that increases the complexity of creating a big data system. Hence, it is not surprising that the demand for experts in this area is steadily increasing. To overcome this problem and the related shortage of required knowledge, in the following paper the concept of a decision support system for the selection of appropriate big data technologies is introduced, in order to implement a given project. Through the use of the design science research methodology a preliminary artifact was developed that provides sophisticated recommendations as well as architectural models and blank systems to support the systems engineering procedure.

Keywords

Schlüsselwörter

Big Data, Technologies, Decision Support, System, Design Science

References

Referenzen

1. Yin, S., Kaynak, O.: Big Data for Modern Industry: Challenges and Trends [Point of View]. Proc. IEEE 103, 143–146 (2015)

2. Chen, C.L.P., Chun-Yang, Z.: Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences 275, 314–347 (2014)

3. Turck, M. and Obayomi, D.: The Big Data Landscape, http://dfkoz.com/big-datalandscape/

4. Volk, M., Pohl, M., Turowski, K.: Classifying Big Data Technologies – An Ontologybased Approach. In: 24rd Americas Conference on Information 2018. AIS, New Orleans (2018)

5. Volk, M., Jamous, N., Turowski, K.: Ask the Right Questions – Requirements Engineering for the Execution of Big Data Projects. In: 23rd Americas Conference on Information Systems. AIS, Boston (2017)

6. Alharthi, A., Krotov, V., Bowman, M.: Addressing barriers to big data. Business Horizons 60, 285–292 (2017)

7. Volk, M., Staegemann, D., Pohl, M., Turowski, K.: Challenging Big Data Engineering: Positioning of Current and Future Development. In: Proceedings of 4th International Conference on Internet of Things, Big Data and Security, pp. 351–358. SCITEPRESS – Science and Technology Publications

8. Lau, R.Y.K., Liao, S.S.Y., Wong, K.F., Chiu, D.K.W.: Web 2.0 Environmental Scanning and Adaptive Decision Support for Business Mergers and Acquisitions. MIS Quaterly 36, 1239–1268 (2012)

9. Lee, H., Aydin, N., Choi, Y., Lekhavat, S., Irani, Z.: A decision support system for vessel speed decision in maritime logistics using weather archive big data. Computers & Operations Research 98, 330–342 (2018)

10. Hevner, A.R., March, S.T., Park, J., Ram, S.: Design Science in Information Systems Research. MIS Quaterly 28, 75–105 (2004)

11. Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A design science research methodology for information systems research. Journal of management information systems 24, 45–77 (2007)

12. Nunamaker, J.F., Chen, M., Purdin, T.D.M.: Systems Development in Information Systems Research. Journal of management information systems 7, 89–106 (1990)

13. Turban, E., Aronson, J.E., Liang, T.-P.: Decision support systems and intelligent systems. Prentice-Hall; Pearson, Upper Saddle River (op. 2005)

14. Power, D.J.: Decision support systems. Concepts and resources for managers. Quorum Books, Westport, Conn. (2002)

15. Shim, J.P., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R., Carlsson, C.: Past, present, and future of decision support technology. Decision Support Systems 33, 111–126 (2002)

16. Volk, M., Hart, S.W., Bosse, S., Turowski, K.: How much is Big Data? A Classification Framework for IT Projects and Technologies. In: 22nd Americas Conference on Information Systems, AMCIS 2016, San Diego, CA, USA, August 11-14, 2016. Association for Information Systems (2016)

17. Portela, F., Lima, L., Santos, M.F.: Why Big Data? Towards a Project Assessment Framework. Procedia Computer Science 98, 604–609 (2016)

18. Fekete, D., Vossen, G.: The GOBIA Method: Towards Goal-Oriented Business Intelligence Architectures (2015)

19. Lehmann, D., Fekete, D., Vossen, G.: Technology selection for big data and analytical applications. ERCIS – European Research Center for Information Systems, Münster (2016)

20. Lněnička, M.: AHP Model for the Big Data Analytics Platform Selection. Acta Informatica Pragensia 4, 108–121 (2015)

21. Pääkkönen, P., Pakkala, D.: Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems. Big Data Research 2, 166–186 (2015)

22. Kreps, J.: Questioning the Lambda Architecture. The Lambda Architecture has its merits, but alternatives are worth exploring, https://www.oreilly.com/ideas/questioning-thelambda-architecture

23. Marz, N.: How to beat the CAP theorem, http://nathanmarz.com/blog/how-to-beat-the-captheorem. html

24. Martínez-Prieto, M.A., Cuesta, C.E., Arias, M., Fernández, J.D.: The Solid architecture for real-time management of big semantic data. Future Generation Computer Systems 47, 62–79 (2015)

25. Avvenuti, M., Cresci, S., Del Vigna, F., Fagni, T., Tesconi, M.: CrisMap: a Big Data Crisis Mapping System Based on Damage Detection and Geoparsing. Inf Syst Front 20, 993–1011 (2018)

26. Huang, Q., Cervone, G., Jing, D., Chang, C.: DisasterMapper. In: Chandola, V., Vatsavai, R.R. (eds.) BigSpatial 2015, pp. 1–6. The Association for Computing Machinery, Inc, New York, NY (2015)

27. Ta-Shma, P., Akbar, A., Gerson-Golan, G., Hadash, G., Carrez, F., Moessner, K.: An Ingestion and Analytics Architecture for IoT Applied to Smart City Use Cases. IEEE Internet Things J. 5, 765–774 (2018)

28. NIST Big Data Public Working Group: NIST Big Data Interoperability Framework: Volume 3, Use Cases and General Requirements Version 2. National Institute of Standards and Technology, Gaithersburg, MD (2018)

29. Delibašić, B. (ed.): Decision Support Systems V – big data analytics for decision making. Springer International Publishing, Cham (2015)

30. Elgendy, N., Elragal, A.: Big Data Analytics in Support of the Decision Making Process. Procedia Computer Science 100, 1071–1084 (2016)

31. Aversa, P., Cabantous, L., Haefliger, S.: When decision support systems fail: Insights for strategic information systems from Formula 1. The Journal of Strategic Information Systems 27, 221–236 (2018)

32. Poleto, T., Carvalho, V.D.H. de, Costa, A.P.C.S.: The Roles of Big Data in the Decision-Support Process: An Empirical Investigation. In: Delibašić, B. (ed.) Decision Support Systems V – big data analytics for decision making, 216, pp. 10–21. Springer International Publishing, Cham (2015)

33. Razmak, J., Aouni, B.: Decision Support System and Multi-Criteria Decision Aid: A State of the Art and Perspectives. J. Multi-Crit. Decis. Anal. 22, 101–117 (2015)

34. Sonnenberg, C., Vom Brocke, J.: Evaluations in the Science of the Artificial ‐ Reconsidering the Build-Evaluate Pattern in Design Science Research. In: Peffers, K., Rothenberger, M., Kuechler, B. (eds.) Design Science Research in Information Systems. Advances in Theory and Practice, pp. 381–397. Springer Berlin Heidelberg, Berlin, Heidelberg (2012)

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